OpenThinker-7B-Uncensored-DeLMAT-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Base Model Size | 7B Parameters |
| Model Format | GGUF |
| Original Source | nkpz/OpenThinker-7B-Uncensored-DeLMAT |
What is OpenThinker-7B-Uncensored-DeLMAT-GGUF?
OpenThinker-7B-Uncensored-DeLMAT-GGUF is a quantized version of the OpenThinker model, optimized for efficient deployment and reduced storage requirements. The model offers multiple quantization options, allowing users to balance between model size and performance based on their specific needs.
Implementation Details
The model comes in various quantization formats, from highly compressed Q2_K (3.1GB) to full precision F16 (15.3GB). Notable implementations include Q4_K_S and Q4_K_M variants which are recommended for their balance of speed and quality, and Q8_0 which offers the best quality while maintaining reasonable size requirements.
- Multiple quantization options ranging from 3.1GB to 15.3GB
- Recommended formats: Q4_K_S (4.6GB) and Q4_K_M (4.8GB) for optimal performance
- Q6_K (6.4GB) offers very good quality
- Q8_0 (8.2GB) provides the best quality while maintaining speed
Core Capabilities
- Efficient deployment with various size options
- Optimized for different hardware configurations
- Compatible with standard GGUF implementations
- Supports both high-performance and resource-constrained environments
Frequently Asked Questions
Q: What makes this model unique?
The model provides a comprehensive range of quantization options, making it highly adaptable to different deployment scenarios while maintaining quality. The availability of both standard and IQ-quants makes it particularly versatile.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants are recommended as they offer an excellent balance of speed and quality. For scenarios requiring maximum quality, the Q8_0 variant is recommended, while resource-constrained environments might benefit from the smaller Q2_K or Q3_K_S variants.
